ArticleJournal of clinical laboratory analysis2016
Diagnostic Algorithm for the Diagnosis of Pediatric Parasitic Gastroenteritis.
Article in Journal of clinical laboratory analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent practices for ordering stool studies in patients with abdominal and gastrointestinal symptoms are not standardized. We hypothesized that an algorithm involving first-line use of a Cryptosporidium/Giardia combination antigen test and stricter use of ova and parasite (O&P) examinations would be clinically and cost effective.
methodsIn this study, stool O&P test results for pediatric patients in Dallas, Texas, were reviewed. All results obtained between 2009 and 2012 were included. Patient charts were reviewed to determine test results, symptoms, treatment, travel, and past medical history. Using these data, a retrospective modeling study was done to evaluate the utility of a diagnostic algorithm that limits O&P testing to those patients who are immunocompromised or have travelled outside the United States.
resultsOver the 3-year period of this study, we found that the prevalence of gastrointestinal parasitic disease in children was 1.9%. Analysis of the diagnostic algorithm for the judicious use of stool O&P showed that as much as 65% of testing may be unnecessary and could be eliminated.
conclusionsOur findings show that the prevalence of pediatric gastrointestinal parasitic disease in Texas may be lower than expected. In addition, these data show that a diagnostic algorithm limiting O&P testing may be both clinically and cost effective in low-prevalence settings. However, such an algorithm would miss a significant number of infections due to Dientamoeba fragilis and Blastocystis hominis.
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